Predict method for
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an object of class
An optional data frame in which to look for variables with which to predict.
If omitted, the environmental variables at the sites in
further arguments passed to
The predictor cumulative functions can be used to transform grid data layers of environmental variables to a common biological importance scale. This transformation of the multi-dimensional environment space is to a biological space in which coordinate position represents composition associated with the predictors. These inferred compositional patterns can be mapped in biological space and geographic space in a manner analogous to ordination, that takes into account the non-linear and sometimes threshold changes that occur along gradients.
Where environmental values lie outside the range of the original site data, by default extrapolation
is performed. That is, if
(xmin,xmax) are the range of the site predictors with corresponding
cumulative importance values
(ymin,ymax), the prediction
y at a new environmental value
outside the range
ymin + (y-ymin)*(x-xmin)/(xmax-xmin).
This is equivalent to assigning the average importance inside
(xmin,xmax) to all values
outside the range. If
extrap=FALSE, linear extrapolation is not performed; instead predictions
xmin are fixed at
ymin and predictions above
xmax are fixed at
This is equivalent to assigning zero importance outside the range of the site data.
an object of class
predict.gradientForest. It is a dataframe in which each predictor
has been transformed to the biological scale by the cumulative importance
function, as defined by
N. Ellis, CSIRO, Cleveland, Australia. <[email protected]>
Ellis, N., Smith, S.J., and Pitcher, C.R. (2012) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.
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